COVID-19 not only generated challenges but also brought new chances in the construction industry of Nepal. The goal of this research is to investigate opportunities created by COVID-19 in the construction industry of Nepal. For this, a total of 309 responses from clients, consultants and contractors were obtained. Principal Component Analysis was deployed and major opportunities obtained were technological transformation, and health and safety culture improvisation with variance of 31.46% and 20.14% respectively. It is recommended that policies should be prepared to make technology use and change adopted sustainable and long-lasting. Also, use of advanced technologies and scientific planning should be encouraged by the government and strategies should be prepared so that difficult situations like pandemic could also be utilized in fruitful ways.

Steel portal frames, used in factories, workshops, shopping complexes, and warehouses, provide large clear spans. Eurocode 3, a semi-probabilistic design code based on limit state design and adopted in Nigeria, does not fully address uncertainties in load and resistance variables, affecting frame performance in service. This study evaluates the reliability of three-hinged steel portal frames by analyzing three primary failure modes: flexural instability of frame stanchions (failure mode 1), flexural instability of frame rafters (failure mode 2), and lateral torsional instability of stanchions and rafters (failure mode 3). Limit state functions for these failure modes were derived from Eurocode 3 specifications and structural analysis load effects. Stochastic models of uncertain parameters are obtained from the literature. The First-Order Reliability Method (FORM), using a MATLAB program, evaluates the limit state functions and determines failure probability. Results show that for failure mode 1, Eurocode's target reliability of 3.8 is met if Xc is at least 0.85 for stanchions and 0.6 for rafters. At flexural buckling (XLT = 1.0), lateral-torsional stability yields a safety index of 5.8. For failure modes 1 and 2, the safety index decreases with a higher dead load to variable load ratio. For all failure modes, the safety index increases with higher steel grades and coefficient of variation (CoV). To ensure safety, the study recommends fully accounting for uncertainties in design to prevent up to a 60% compromise in portal frame safety.

Tuffcrete concrete (ATC) has emerged as a promising material in modern civil engineering due to its enhanced durability and eco-friendly composition. This study presents the development of Artificial Neural Network (ANN) models to predict and optimize the compressive strength of Tuffcrete concrete based on experimental data. The dataset consists of 21 input features, including mix proportions (e.g., cement content, water-cement ratio, aggregate size distribution), material properties (e.g., tuffcrete polymer, slag content), and process parameters (e.g., mixing time, compaction level). The ANN models were trained and validated using these features to accurately forecast the compressive strength of Tuffcrete concrete under various conditions. The study demonstrates the model's ability to capture nonlinear relationships between input variables and compressive strength, achieving high accuracy metrics (e.g., R² and RMSE). Furthermore, optimization techniques were employed to identify the optimal mix design for maximizing compressive strength. Results reveal critical insights into the interplay between material properties and mechanical performance, paving the way for efficient mix designs tailored for specific applications. This work contributes to the advancement of machine learning applications in civil engineering, providing a robust framework for performance prediction and optimization of sustainable construction materials.